非参数不确定系统的有限时间迭代学习控制

针对任意初态情形，引入初始修正作用，研究一类非参数不确定时变系统能够达到实际完全跟踪性能的迭代学习控制方法. 采用Lyapunov-like综合，设计迭代学习控制器处理不确定性时变系统非参数化问题，其中含有有限时间控制作用，以实现在预先指定区间上的零误差跟踪. 并且，运用完全限幅学习机制，保证闭环系统中各变量的一致有界性以及跟踪误差的一致收敛性. 仿真结果表明了所提出控制方法的有效性.

Finite-time Iterative Learning Control for Systems with Nonparametric Uncertainties

This paper presents an iterative learning control approach for systems with nonparametric uncertainties, which achieves practical complete tracking in the presence of arbitrary initial state errors. Based on Lyapunov-like synthesis, a learning controller is designed for handling uncertainties, without any parametrization. For the controller design, an initial rectifying action is introduced such that the tracking error will converge to zero over a pre-specified interval as iteration increases. With the fully-saturated learning mechanisms, the uniform boundedness of all variables in the closed-loop and, in turn, the uniform convergence of the tracking error are guaranteed. The effectiveness of the proposed control method is demonstrated by the presented numerical results.